Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models
This provides a method for redshift estimation from imaging data, which could help unlock the potential of wide-field surveys by reducing reliance on costly spectroscopic methods, though it appears incremental as it builds on existing diffusion models for a specific domain.
The paper tackled the problem of estimating galaxy redshifts from imaging data by using a diffusion model conditioned on continuous redshift to capture morphology-redshift correlations, achieving generated images that closely correlate with true redshifts on test data from the HyperSuprime-Cam survey.
Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about $1000$ times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscopic surveys out of reach. Photometric redshift methods rely on imaging in multiple color filters and template fitting, yet they ignore the wealth of information carried by galaxy shape and structure. We demonstrate that a diffusion model conditioned on continuous redshift learns this missing joint structure, reproduces known morphology-$z$ correlations. We verify on the HyperSuprime-Cam survey, that the model captures redshift-dependent trends in ellipticity, semi-major axis, Sérsic index, and isophotal area that these generated images correlate closely with true redshifts on test data. To our knowledge this is the first study to establish a direct link between galaxy morphology and redshift. Our approach offers a simple and effective path to redshift estimation from imaging data and will help unlock the full potential of upcoming wide-field surveys.